Many websites are configured as online catalogs. These catalogs act as alternatives to traditional paper catalogs and offer enhanced navigational features when compared to their paper counterparts as well as the advantages of broad, easy distribution. Through the Internet, the market base of manufacturers and resellers may be maximized while associated overhead may potentially be drastically reduced. Well-organized electronic catalogs help consumers make good purchasing decisions by providing extensive information about products in an easy-to-navigate manner. Such catalogs allow consumers to gain information about products and to purchase products directly. Additionally, such catalogs serve as a site where companies may purchase advertising to market their products.
Electronic catalogs generally store, in a database, information about a number of products which may be, for example, electronics, housewares, apparel, digital content, or any other type of item which may be depicted and/or described electronically. Such items may be described by a taxonomy, which describes the set of products with a set of information that consists of a set of attributes that assume values. That is, each product may be associated with a price, brand, and other attributes. Some attributes may only be stored for some classes of product. For example, weight might be an attribute of laptops, but not desktop computers, while both might have a processor speed attribute.
Once a retailer or other content provider has provided a taxonomy for its products, it remains for the users of the catalog system to retrieve the products using the taxonomy system. One way to do this is by performing searches using filters. Filters may be composed of product attributes and possible attribute values which a user may select to narrow the products in a taxonomy. Such filters constrain the allowable values of the attributes, and thereby generate a more manageable subset of the products that the user may use, manipulate, and digest.
Filters allow the users to reduce the potentially huge numbers of products which otherwise occupy catalogs and reduce them to manageable numbers. They also allow users to focus their searches to meet their individualized needs, as well as incorporate factors such as ability to pay or brand requirements due to purchasing contracts. However, conventional electronic catalogs prevent a user from knowing to what extent selection of a filter attribute value will affect a set of search results. A user may filter an electronic catalog to such an extent that no products match the user's selected attribute values. Likewise, a user may select an attribute value that insufficiently narrows or fails to narrow a set of search results at all. These results can be inconvenient to a user and prolong the search or filter process.
Websites such as AMAZON.COM™ and EBAY.COM™ provide filters that allow a user to focus their searches by selecting attribute values. For the convenience of a user, both AMAZON.COM™ and EBAY.COM™ provide a count after attribute values indicating how many records satisfy that filter. For example, a search for “Hemingway” on AMAZON.COM™ that generated 6,724 results provided filters to narrow the results. An attribute “Books” included values such as “Literature & Fiction (3,056)” and “Reference (239)” and an attribute “Binding” included values such as “Paperback (2,584)” and “Board Book (12)”, with the parenthetical number indicating a count of records satisfying a filter. Likewise, a search for “Hemingway” on EBAY.COM™ that generated 1,906 results provided filters to narrow the results. An attribute “Books (1,680)” included values such as “Fiction & Literature (609)” and “Nonfiction (578)” and an attribute “Buying Formats” included “Auction” and “Buy it Now”.
While it may be convenient for a user to see a static count of records satisfying each filter before the user selects a filter, additional processing time may be required to generate counts of records satisfying each filter before displaying the results of the user search. Additionally, because products may quickly sell out, the static count of records satisfying a filter may be inaccurate by the time a user decides to select a filter. Further, the static number of records satisfying a filter provides no indication of how many records may satisfy a combination of filters. For example, while a search of AMAZON.COM™ for “Hemingway” resulted in 12 books having the value “Board Book” for the attribute “Binding” and 239 books having the value “Reference” for the attribute “Books”, no books satisfy both the filters “Reference” and “Board Book”. Thus, a user must focus a search (e.g., drill down) using filters before realizing that a desired combination of filters may have no matching records, too few matching records, or too many matching records.
While the system and method is described herein by way of example and embodiments, those skilled in the art recognize that generating an automatic catalog search preview is not limited to the embodiments or drawings described. It should be understood that the drawings and description are not intended to limit embodiments to the particular form disclosed. Rather, the intention is to cover all modifications, equivalents and alternatives falling within the spirit and scope of the invention defined by the appended claims. Any headings used herein are for organizational purposes only and are not meant to limit the scope of the description or the claims. As used herein, the word “may” is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words “include”, “including”, and “includes” mean including, but not limited to.